本文是谷歌发表在NeurIPS 2012上的一篇论文,主要讨论了在几万个CPU节点上训练大规模深度网络的问题,并提出了一个名为DistBelief的软件框架.在该框架下实现了两种大规模分布式训练算法:Downpour SGD和Sandblaster L-BFGS,这两种算法都增加了深度网络训练的规模和速度. Introduction 最近几年,深度学习在语音识别.图像识别以及自然语言处理等领域大放异彩.就训练样本的数量和模型参数的数量而言,增加深度学习的规模可以极大地提高最终模型的效果.GPU的…

HOME ABOUT CONTACT SUBSCRIBE VIA RSS   DEEP LEARNING FOR ENTERPRISE Distributed Deep Learning, Part 1: An Introduction to Distributed Training of Neural Networks Oct 3, 2016 3:00:00 AM / by Alex Black and Vyacheslav Kokorin Tweet inShare27   This pos…

郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017, Fort Lauderdale, Florida, USA. JMLR: W&CP volume 54. Copyright 2017 by the author(s). Abstract 现代移动设备可以访问大量适合模型学…

In recent years, there’s been a resurgence in the field of Artificial Intelligence. It’s spread beyond the academic world with major players like Google, Microsoft, and Facebook creating their own research teams and making some impressive acquisition…

BigDL: Distributed Deep Learning on Apache Spark What is BigDL? BigDL is a distributed deep learning library for Apache Spark; with BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of e…

B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, "Communication-Efficient Learning of Deep Networks from Decentralized Data," in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Apr. 2017…

Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation Google  2016.10.06 官方 Blog 链接:https://research.googleblog.com/2016/10/graph-powered-machine-learning-at-google.html 今天讲的是一个基于 streaming approximation 的大规模分布式半监督学习框架,出自 Goo…

Initialization of deep networks 24 Feb 2015Gustav Larsson As we all know, the solution to a non-convex optimization algorithm (like stochastic gradient descent) depends on the initial values of the parameters. This post is about choosing initializati…

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks ICML 2017 Paper:https://arxiv.org/pdf/1703.03400.pdf Code for the regression and supervised experiments:https://github.com/cbfinn/maml Code for the RL experiments:https://github.com/cb…

Rupesh Kumar SrivastavaKlaus Greff ̈J urgenSchmidhuberThe Swiss AI Lab IDSIA / USI / SUPSI{rupesh, klaus, juergen}@idsia.ch AbstractTheoretical and empirical evidence indicates that the depth of neural networksis crucial for their success. However, t…